Statistical shape analysis of brain structures using spherical wavelets

D. Nain, M. Styner, M. Niethammer, J. J. Levitt, M. E. Shenton, G. Gerig, A. Bobick, A. Tannenbaum

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. As an application, we analyze two brain structures, the caudate nucleus and the hippocampus, and compare the results obtained to shape analysis using a sampled point representation. Our results show that the SWC representation indicates new areas of significance preserved under the FDR correction for both the left caudate nucleus and left hippocampus. Additionally, the spherical wavelet representation provides a natural way to interpret the significance results in terms of scale in addition to knowing the spatial location of the regions.

    Original languageEnglish (US)
    Title of host publication2007 4th IEEE International Symposium on Biomedical Imaging
    Subtitle of host publicationFrom Nano to Macro - Proceedings
    Pages209-212
    Number of pages4
    DOIs
    StatePublished - 2007
    Event2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI'07 - Arlington, VA, United States
    Duration: Apr 12 2007Apr 15 2007

    Publication series

    Name2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings

    Other

    Other2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI'07
    CountryUnited States
    CityArlington, VA
    Period4/12/074/15/07

    Keywords

    • Image shape analysis
    • Wavelet transforms

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering
    • Medicine(all)

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